Abstract The development of lung cancer is among the best-known examples of gene-environment interaction, whereby lung cancer development is infrequent in nonsmokers but common in smokers, and heritability analysis clearly indicates a considerable role of genetic factors in explaining lung cancer risk. However, assembling a large enough sample to document gene-environment interactions has been challenging. Our proposal will bring together most of the world's studies on lung cancer genetics and smoking allowing many investigators to benefit from our efforts. In order to release the benefits that occur from low dose spiral CT screening a more targeted approach to risk evaluation is likely to be important given the high number of positive findings on screening and the high cost of screening to find affected individuals. We are proposing this supplement to allow us to add data to dbGAP and to ensure that existing data already available to dbGAP as well as data we have uploaded is imputed using greatly improved referent panels. This improved imputation will facilitate gene- gene and gene-environment interaction analyses and allow us to perform functional annotations more precisely. The specific aims of the supplement are: 1. To upload data from an Affymetrix Axiome array analysis of 12,260 participants comprised of cases and controls from 9 sites. The data to be uploaded will include genotypes, demographics, smoking behavior, and histology. 2. To impute data from all available genomic studies including the Oncoarray, Affymetrix Axiome Array, African-American Studies, and never smoker American GWAS. Imputing these data to a newer common standard will be valuable to the parent project by allowing more precise imputation of rare variants, which play an important role in lung cancer risk. Additionally, the scientific community at large will benefit from having access to these data because it will support better gene-gene and gene-environment interaction analyses across a large number of samples and for multiple ethnicities.